Using Domain Ontology and Sequential Rule Mining for Extracting Behavior Patterns from Web Navigation Logs

Abstract

Due to unprecedented growth of information on the Web and lack of structure in many Web sites, it became real challenge to the Web users to find relevant information. To solve this problem, Personalization becomes a popular solution to customize the World Wide Web environment toward the user's preferences. Recent studies show that Web Usage Mining plays an important role in designing recommendation systems. Classical Web Usage Mining does not take Semantics Knowledge into pattern discovery and recommendation process. Recent studies show that Ontology as domain knowledge can improve pattern's quality. Our work aims to incorporate semantics knowledge into Web Usage Mining process. ERMiner, a state-of-the art algorithm for Sequential rule mining is applied over the Semantic space to generate frequent Sequential rules. Experimental results shown are promising and proved that incorporating Semantic Knowledge into Web Usage Mining process can provide us with more quality patterns which consequently make the recommendation system more functional, smarter and comprehensive. The experimental results of our Web recommendation system show a significant improvement on the quality of the recommendations.